Genetic Contributors to the Impact of Sex on Heterogeneity in Flu Infection

性别对流感感染异质性影响的遗传因素

基本信息

批准号：
10663342
负责人：
Dennis Chun-Yone Ko
金额：
$ 51.72万
依托单位：
DUKE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-07-11 至 2027-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10663342
关键词：
1918 influenza pandemic African Alleles Area Asian Bacterial Infections Biological Markers Cell Line Cell model Cells Cessation of life Child Clinical Research Clustered Regularly Interspaced Short Palindromic Repeats Communicable Diseases Communities Complex Computer Analysis Computing Methodologies Coupled Curettage procedure Data Set Databases Development Disease Environment European Exhibits Female Female of child bearing age Gene Expression Gene Expression Profile Genes Genetic Genetic Transcription Genetic Variation Genotype Goals Heterogeneity Hospitalization Human Human Genetics Hypoxia Immune response In Vitro Individual Infection Influenza Influenza A virus Information Dissemination Integration Host Factors Internet Link Machine Learning Measures Modeling Nose Outcome Persons Phenotype Population Predisposition Publishing Quantitative Trait Loci RNA Interference RNA Splicing RNA interference screen Research Resolution Resources Respiratory Tract Infections Risk SARS coronavirus Sampling Severities Sex Bias Sex Differences Symptoms Testing Time Transcript Tube United States Validation Variant Viral Virus Diseases Virus Replication biobank candidate identification computational pipelines computing resources constitutive expression data dissemination diagnostic strategy differential expression experience flu gene interaction genome wide association study influenza infection insight lymphoblastoid cell line male multidisciplinary novel diagnostics novel therapeutic intervention overexpression pandemic disease peripheral blood response sex small molecule specific biomarkers transcriptomics virus genetics volunteer web-based tool

项目摘要

Project Summary/Abstract The 1918 influenza pandemic is estimated to have killed 1 in 20 people worldwide. Influenza A virus (IAV) infections usually do not cause such severe disease for the ~30 million infected every year in the United States alone (2014-2015). However, there are broad differences in IAV susceptibility and severity, with outcomes from asymptomatic infections (~16%) to death (0.2% in 2014-2015). These differences arise from the complex interplay of exposure, environment, IAV genetics, and host factors. A crucial host factor that contributes to heterogeneity of IAV infection is sex. For children and older individuals, males are more likely to experience severe disease, while females of child-bearing age have greater severity. As there is strong evidence for 1) the importance of sex in IAV infection, 2) gene expression differences between males and females, and 3) human genetic variation impacting infectious disease in general and specifically IAV infection, synthesis of these three areas may provide crucial mechanistic insight. We hypothesize that sex differences in gene expression are a major driver of heterogeneity in IAV infection. To elucidate these differences, this project will integrate cutting-edge approaches to identify sex-specific differences in transcript abundance and splicing that regulate IAV burden and host response in human cells, IAV challenge volunteers, and natural populations. Further, we will define the genotype x sex interactions that form the mechanistic basis for how genetic diversity contributes to sex differences in IAV infection. To achieve these goals, we have unique datasets of IAV infection heterogeneity in cells from dozens of male and female donors, in nasal curettage and peripheral blood from human IAV challenge subjects, and biobanked samples of natural IAV infection with outcomes ranging from mild infection to death. Computational analyses of these datasets will define 1) sex differences in gene expression that correlate with IAV burden and symptom severity and 2) human SNPs that regulate sex-biased gene expression and flu severity. The transcriptional profiles from these datasets will be used to generate sex-specific biomarkers of IAV infection severity using machine learning approaches. Finally, we will experimentally determine whether the identified sex-biased genes and SNPs regulate IAV burden and host response in cellular models of infection. All results will be available through an easy-to-use web database for exploring this rich dataset as a launchpad for further mechanistic and clinical studies. This project will develop and apply computational methods to generate a high-resolution analysis of how sex and genes interact to impact IAV infection. Understanding the genetic basis for sex differences in IAV infection could lead to new diagnostic approaches in identifying at-risk individuals and novel therapeutic strategies.

项目摘要/摘要据估计，1918年流感大流行病已在全球20人中丧生1人。流感病毒（IAV）在美国，感染通常不会引起约3000万感染的严重疾病单独（2014-2015）。但是，IAV的敏感性和严重程度存在很大差异，结果无症状感染（约16％）死亡（2014- 2015年为0.2％）。这些差异来自复合物暴露，环境，IAV遗传学和宿主因素的相互作用。有助于IAV感染异质性的关键宿主因素是性别。适合儿童及以上的儿童个体，男性更有可能患上严重疾病，而育儿的女性则更大严重程度。由于有强烈的证据表明1）性别在IAV感染中的重要性，2）基因表达差异在男性和女性之间，以及3）人类遗传变异总体上影响传染病，特别是IAV感染，这三个区域的合成可能会提供至关重要的机械洞察力。我们假设基因表达的性别差异是IAV感染中异质性的主要驱动力。阐明这些差异，该项目将整合尖端的方法以识别成绩单的性别差异调节人类细胞中IAV负担和宿主反应的丰度和剪接，IAV挑战志愿者，和自然种群。此外，我们将定义构成机械基础的基因型X性相互作用遗传多样性如何促进IAV感染的性别差异。为了实现这些目标，我们在数十个细胞中拥有IAV感染异质性的独特数据集男性和女性捐助者，鼻脊椎和人类IAV挑战受试者的外周血，以及自然IAV感染的生物群样品，其结局从轻度感染到死亡。计算对这些数据集的分析将定义1）与IAV负担相关的基因表达性别差异症状严重程度和2）调节性偏见基因表达和流感严重程度的人SNP。这这些数据集的转录概况将用于产生IAV感染的性别特异性生物标志物使用机器学习方法的严重性。最后，我们将通过实验确定是否已确定性别偏见的基因和SNP调节IAV负担和宿主反应在感染的细胞模型中。所有结果将通过易于使用的Web数据库提供，以探索这个富数据集作为启动板，以便进一步机械和临床研究。该项目将开发并应用计算方法，以生成有关如何的高分辨率分析性别和基因相互作用以影响IAV感染。了解IAV性别差异的遗传基础感染可能导致新的诊断方法在识别高危个人和新型治疗方面策略。